Incidence and Longitudinal Changes in the Prevalence of Diabetes among Rural Residents of Saskatchewan, Canada
Bibliographic record
Abstract
Background: Saskatchewan is one of the largest rurally populated provinces in Canada with a high prevalence of diabetes in the rural population. Current knowledge about risk factors of diabetes prevalence among the rural residents and the First Nations populations in Saskatchewan is primarily based on cross-sectional studies. Additionally, information regarding risk factors associated with the incidence of diabetes among Canadian rural and First Nation populations is limited. Purpose: The purpose of this study was to extensively assess predictors and longitudinal changes associated with the incidence and prevalence of diabetes among rural residents and First Nations in Saskatchewan, Canada. Additionally, we wanted to determine differences in rates and risk factors of diabetes between First Nations and rural residents in Saskatchewan. Methods: Both the Saskatchewan rural health study (SRHS) and First Nation Lung Health Project (FNLHP) were prospective cohort studies. SRHS was conducted in two phases: a baseline survey (2010, n=8261) and a follow-up survey (2014, n=4867). The FNLHP was also conducted in two phases: baseline survey (2012/13, n=874) and a follow-up survey (2016, n=839) Results: The prevalence of diabetes increased from the baseline to follow-up in rural residents and First Nation populations, and non-farm rural residents had a higher prevalence of diabetes than rural farm residents. Apart from common modifiable risk factors, agricultural chemical-related exposures were responsible for the high prevalence of diabetes among rural residents but proven non-significant for the incidence of diabetes. A unique finding of our study was that sleep apnea significantly increases the risk of developing new diabetes cases among rural residents, which is non-significant for the prevalence of diabetes. Additionally, diabetes was prevalent among First Nation women, compared to men, and the finding was reversed for rural residents. Unemployment and high BMI were the most significant factors for the high prevalence of diabetes among First Nation Populations. However, severe perceived discrimination acted as a protective factor for diabetes prevalence, and the underlying mechanism was unclear. Conclusion: Both individual and contextual factors were responsible for the high incidence and prevalence of diabetes among the rural residents and First Nation populations of Saskatchewan, which demands urgent long term and population-based community health initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".